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应用机器学习来开发抗生素和预测微生物耐药性

Apurva Panjla¹, Saurabh Joshi¹, Geetanjali Singh¹

¹Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India.

Chemistry, an Asian journal

|July 1, 2024

概括

抗菌素耐药性 (AMR) 是一个全球性的健康危机. 机器学习加速了抗生素的发现,并预测了耐药性模式,为打击这种日益增长的威胁提供了至关重要的工具.

科学领域:

计算生物学是一种计算生物学.
药物发现药物发现
传染性疾病传染性疾病

背景情况:

抗菌素耐药性 (AMR) 是全球卫生面临的重大挑战.
抗生素发现的速度落后于AMR的快速出现.
开发新的抗生素是昂贵和耗时的.

研究的目的:

审查机器学习 (ML) 在抗生素药物发现中的变革潜力.
突出ML在预测AMR模式和药物代谢中的应用.
支持研究人员利用ML开发新型抗生素.

主要方法:

审查用于药物发现的机器学习算法最近的进展.
分析ML在识别新抗生素支架中的作用.
探索ML在预测抗菌素耐药性和药物动力学特性方面的实用性.

主要成果:

机器学习提供了强大的工具,以加快新型抗生素类别的识别.
机器学习算法可以更有效地预测抗菌素耐药性模式.
ML有助于了解药物代谢,这对于有效的抗生素开发至关重要.

结论:

机器学习是解决AMR危机的关键,通过增强抗生素发现管道.
整合ML可以显著降低与抗生素开发相关的成本和时间.
未来的研究应该专注于进一步利用ML来对抗传染病.

关键词:

抗微生物耐药性抗微生物耐药性抗生素抗生素是一种抗生素.抗微生物是一种抗微生物.机器学习是机器学习.

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